# Clear workspace
rm(list = ls(all.names = TRUE))

# Load libraries
library(ggplot2); library(dplyr); library(sjPlot);library(metafor); library(ggpubr); library(tools); library(readxl); library(purrr); library(ggstatsplot)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
## 
## Attaching package: 'sjPlot'
## The following object is masked from 'package:ggplot2':
## 
##     set_theme
## Loading required package: Matrix
## Loading required package: metadat
## Loading required package: numDeriv
## 
## Loading the 'metafor' package (version 4.8-0). For an
## introduction to the package please type: help(metafor)
## Registered S3 methods overwritten by 'backports':
##   method                    from 
##   as.character.Rconcordance tools
##   print.Rconcordance        tools
## You can cite this package as:
##      Patil, I. (2021). Visualizations with statistical details: The 'ggstatsplot' approach.
##      Journal of Open Source Software, 6(61), 3167, doi:10.21105/joss.03167
# Load functions
source('./regression_analysis.R')
## Loading required package: MASS
## 
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
## 
##     select
knitr::opts_chunk$set(echo = FALSE, message = FALSE, warning = FALSE)
theme_set(theme_bw())

graph_th <- theme(
      text = element_text(family = "Times New Roman", size = 12),
      axis.text = element_text(family = "Times New Roman", size = 12),
      axis.title = element_text(family = "Times New Roman", size = 12),
      strip.text = element_text(family = "Times New Roman", size = 8),
      plot.margin = unit(c(0.2, 0.2, 0.2, 0.2), "cm"),
      axis.title.y = element_text(margin = margin(r = 2, unit = "mm")),
      axis.title.x = element_text(margin = margin(t = 2, unit = "mm"))
    ) 

Compare the deltas of the three groups

## NULL

DEMOGRAPHICS

## 
##  Pearson's Chi-squared test
## 
## data:  sex_table
## X-squared = 1.3822, df = 2, p-value = 0.501
## 
##  Kruskal-Wallis rank sum test
## 
## data:  educ_years by group
## Kruskal-Wallis chi-squared = 0.33112, df = 2, p-value = 0.8474
## 
##  Kruskal-Wallis rank sum test
## 
## data:  age by group
## Kruskal-Wallis chi-squared = 14.385, df = 2, p-value = 0.0007523
## 
##  Kruskal-Wallis rank sum test
## 
## data:  P1_APOE4_load by group
## Kruskal-Wallis chi-squared = 8.4406, df = 2, p-value = 0.01469
## 
##  Pairwise comparisons using Wilcoxon rank sum test with continuity correction 
## 
## data:  as.numeric(data_explore$P1_APOE4_load) and data_explore$group 
## 
##          CU A- V- CU A- V+
## CU A- V+ 0.954    -       
## CU A+ V- 0.012    0.161   
## 
## P value adjustment method: fdr
##     n age_mean  age_sd educ_years_mean educ_years_sd sex_f sex_m apoe4_carriers
## 1 140 55.77143 6.37028        13.87857      3.595496    79    61             29
##    n age_mean   age_sd educ_years_mean educ_years_sd sex_f sex_m apoe4_carriers
## 1 23 60.78261 8.382647        13.30435      3.547532    11    12             11
##    n age_mean   age_sd educ_years_mean educ_years_sd sex_f sex_m apoe4_carriers
## 1 14 61.35714 7.406932        14.07143      3.561855     6     8              3

Factor analysis

Let’s check if the Cognitive Age Delta is associated with the Education Years, because we tried to remove the effect of this variable in the features that were used to compute the CADs. The associations now should be weaker (smaller correlations and thus smaller effect size). Now, years of educations should in principle not be the variable that explains most variance

(Do not be fooled, these relations are NOT significant!!!)

We should check how informative sex, APOE4 load, and education years are of CAD all together, independent of each group.

What happens when we check the covariate effects by group? We know that education years has an effect, but what about sex and APOE4 load?

Covariate effects in CAD, in CU A- V- participants

## 
## Call:
## glm(formula = delta ~ educ_years_std + sex + P1_APOE4_load_std, 
##     family = gaussian(), data = factor_data_cu)
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        0.05621    0.44713   0.126   0.9002  
## educ_years_std     0.61917    0.29491   2.099   0.0376 *
## sex                0.03797    0.59426   0.064   0.9491  
## P1_APOE4_load_std  0.04755    0.32534   0.146   0.8840  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 12.13649)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1650.6  on 136  degrees of freedom
## AIC: 752.71
## 
## Number of Fisher Scoring iterations: 2

Covariate effects in CAD, in CU A+ V- participants

## 
## Call:
## glm(formula = delta ~ educ_years_std + sex + P1_APOE4_load_std, 
##     family = gaussian(), data = factor_data_cu_a_pos_vasc_neg)
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0.15764    1.26858   0.124    0.902
## educ_years_std     0.11121    0.84179   0.132    0.896
## sex                0.52161    1.67309   0.312    0.759
## P1_APOE4_load_std -0.09074    0.64739  -0.140    0.890
## 
## (Dispersion parameter for gaussian family taken to be 14.88315)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 282.78  on 19  degrees of freedom
## AIC: 132.98
## 
## Number of Fisher Scoring iterations: 2

Covariate effects in CAD, in CU A- V+ participants

## 
## Call:
## glm(formula = delta ~ educ_years_std + sex + P1_APOE4_load_std, 
##     family = gaussian(), data = factor_data_cu_a_neg_vasc_pos)
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)        1.19178    0.87020   1.370    0.201
## educ_years_std    -0.44772    0.67735  -0.661    0.524
## sex               -1.87128    1.33543  -1.401    0.191
## P1_APOE4_load_std -0.09714    0.72330  -0.134    0.896
## 
## (Dispersion parameter for gaussian family taken to be 5.661516)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 56.615  on 10  degrees of freedom
## AIC: 69.291
## 
## Number of Fisher Scoring iterations: 2

No associations of CAD with education years controling for sex and APOE4 load.

Motor Scales

CU A- V-

## 
## Call:
## glm(formula = delta ~ TINETTI_EQUILIBRIO_std, family = gaussian(), 
##     data = data_motor)
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)
## (Intercept)             0.07512    0.29522   0.254    0.800
## TINETTI_EQUILIBRIO_std  0.38420    0.29628   1.297    0.197
## 
## (Dispersion parameter for gaussian family taken to be 12.20187)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1683.9  on 138  degrees of freedom
## AIC: 751.51
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ TINETTI_MARCHA_std, family = gaussian(), 
##     data = data_motor)
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)         0.07512    0.29700   0.253    0.801
## TINETTI_MARCHA_std -0.03942    0.29806  -0.132    0.895
## 
## (Dispersion parameter for gaussian family taken to be 12.34898)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1704.2  on 138  degrees of freedom
## AIC: 753.19
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ UPDRS_PUNTUACIONTOTAL_std, family = gaussian(), 
##     data = data_motor)
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)
## (Intercept)                0.07512    0.29594   0.254    0.800
## UPDRS_PUNTUACIONTOTAL_std  0.29812    0.29700   1.004    0.317
## 
## (Dispersion parameter for gaussian family taken to be 12.26103)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1692.0  on 138  degrees of freedom
## AIC: 752.19
## 
## Number of Fisher Scoring iterations: 2

No associations below statistical significance, thus, we do not correct for covariates.

CU A+ V-

## 
## Call:
## glm(formula = delta ~ TINETTI_EQUILIBRIO_std, family = gaussian(), 
##     data = data_motor)
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)
## (Intercept)              0.3349     0.7461   0.449    0.658
## TINETTI_EQUILIBRIO_std  -0.8576     0.7629  -1.124    0.274
## 
## (Dispersion parameter for gaussian family taken to be 12.8044)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 268.89  on 21  degrees of freedom
## AIC: 127.82
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ TINETTI_MARCHA_std, family = gaussian(), 
##     data = data_motor)
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)          0.3349     0.7195   0.465    0.646
## TINETTI_MARCHA_std  -1.2618     0.7357  -1.715    0.101
## 
## (Dispersion parameter for gaussian family taken to be 11.90691)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 250.05  on 21  degrees of freedom
## AIC: 126.15
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ UPDRS_PUNTUACIONTOTAL_std, family = gaussian(), 
##     data = data_motor)
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)
## (Intercept)                 0.3349     0.7664   0.437    0.667
## UPDRS_PUNTUACIONTOTAL_std   0.2466     0.7837   0.315    0.756
## 
## (Dispersion parameter for gaussian family taken to be 13.51116)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 283.73  on 21  degrees of freedom
## AIC: 129.06
## 
## Number of Fisher Scoring iterations: 2

CU A- V+

## 
## Call:
## glm(formula = delta ~ TINETTI_EQUILIBRIO, family = gaussian(), 
##     data = data_motor)
## 
## Coefficients: (1 not defined because of singularities)
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)          0.3722     0.6206     0.6    0.559
## TINETTI_EQUILIBRIO       NA         NA      NA       NA
## 
## (Dispersion parameter for gaussian family taken to be 5.392821)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 70.107  on 13  degrees of freedom
## AIC: 66.284
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ TINETTI_MARCHA, family = gaussian(), data = data_motor)
## 
## Coefficients: (1 not defined because of singularities)
##                Estimate Std. Error t value Pr(>|t|)
## (Intercept)      0.3722     0.6206     0.6    0.559
## TINETTI_MARCHA       NA         NA      NA       NA
## 
## (Dispersion parameter for gaussian family taken to be 5.392821)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 70.107  on 13  degrees of freedom
## AIC: 66.284
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ UPDRS_PUNTUACIONTOTAL_std, family = gaussian(), 
##     data = data_motor)
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)
## (Intercept)                 0.2495     0.6852   0.364    0.723
## UPDRS_PUNTUACIONTOTAL_std   0.1358     0.7132   0.190    0.852
## 
## (Dispersion parameter for gaussian family taken to be 6.104043)
## 
##     Null deviance: 67.366  on 12  degrees of freedom
## Residual deviance: 67.144  on 11  degrees of freedom
##   (1 observation deleted due to missingness)
## AIC: 64.237
## 
## Number of Fisher Scoring iterations: 2

No associations below statistical significance, thus, we do not correct for covariates.

Takeaway

No significant associations found.

Neurological History

CU A- V-

## 
## Call:
## glm(formula = delta ~ NEUROL_PRSNCE_Neu13, family = gaussian(), 
##     data = delta_neuro_history)
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)          0.08245    0.37677   0.219    0.827
## NEUROL_PRSNCE_Neu13 -0.01938    0.61236  -0.032    0.975
## 
## (Dispersion parameter for gaussian family taken to be 12.35046)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1704.4  on 138  degrees of freedom
## AIC: 753.21
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ CVADIS_PRSNCE_Neu13, family = gaussian(), 
##     data = delta_neuro_history)
## 
## Coefficients: (1 not defined because of singularities)
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)          0.07512    0.29595   0.254      0.8
## CVADIS_PRSNCE_Neu13       NA         NA      NA       NA
## 
## (Dispersion parameter for gaussian family taken to be 12.2617)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1704.4  on 139  degrees of freedom
## AIC: 751.21
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ TRBINJ_PRSNCE_Neu13, family = gaussian(), 
##     data = delta_neuro_history)
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)          0.07356    0.31634   0.233    0.816
## TRBINJ_PRSNCE_Neu13 -0.20261    1.02319  -0.198    0.843
## 
## (Dispersion parameter for gaussian family taken to be 12.30898)
## 
##     Null deviance: 1649.9  on 135  degrees of freedom
## Residual deviance: 1649.4  on 134  degrees of freedom
##   (4 observations deleted due to missingness)
## AIC: 731.34
## 
## Number of Fisher Scoring iterations: 2

CU A+ V-

## 
## Call:
## glm(formula = delta ~ NEUROL_PRSNCE_Neu13, family = gaussian(), 
##     data = delta_neuro_history)
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)          0.05585    1.05995   0.053    0.958
## NEUROL_PRSNCE_Neu13  0.58348    1.53268   0.381    0.707
## 
## (Dispersion parameter for gaussian family taken to be 13.48185)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 283.12  on 21  degrees of freedom
## AIC: 129.01
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ CVADIS_PRSNCE_Neu13, family = gaussian(), 
##     data = delta_neuro_history)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)         0.0003561  0.7031791   0.001    1.000  
## CVADIS_PRSNCE_Neu13 7.6945382  3.3723286   2.282    0.033 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 10.87814)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 228.44  on 21  degrees of freedom
## AIC: 124.07
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ TRBINJ_PRSNCE_Neu13, family = gaussian(), 
##     data = delta_neuro_history)
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)           0.3142     0.8433   0.373    0.714
## TRBINJ_PRSNCE_Neu13  -2.3415     2.7326  -0.857    0.402
## 
## (Dispersion parameter for gaussian family taken to be 13.51222)
## 
##     Null deviance: 266.65  on 20  degrees of freedom
## Residual deviance: 256.73  on 19  degrees of freedom
##   (2 observations deleted due to missingness)
## AIC: 118.17
## 
## Number of Fisher Scoring iterations: 2

### CU A+ V- correcting for sex, education years, and APOE4 load

## 
## Call:
## glm(formula = delta ~ NEUROL_PRSNCE_Neu13 + sex + educ_years_std + 
##     P1_APOE4_load_std, family = gaussian(), data = delta_neuro_history)
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)         -0.27472    1.64541  -0.167    0.869
## NEUROL_PRSNCE_Neu13  0.72403    1.69596   0.427    0.675
## sex                  0.67882    1.74949   0.388    0.703
## educ_years_std       0.07486    0.86471   0.087    0.932
## P1_APOE4_load_std   -0.08213    0.66209  -0.124    0.903
## 
## (Dispersion parameter for gaussian family taken to be 15.55252)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 279.95  on 18  degrees of freedom
## AIC: 134.75
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ CVADIS_PRSNCE_Neu13 + sex + educ_years_std + 
##     P1_APOE4_load_std, family = gaussian(), data = delta_neuro_history)
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)  
## (Intercept)          -0.9812     1.2377  -0.793    0.438  
## CVADIS_PRSNCE_Neu13   8.9827     3.8126   2.356    0.030 *
## sex                   1.4709     1.5558   0.945    0.357  
## educ_years_std       -0.2225     0.7692  -0.289    0.776  
## P1_APOE4_load_std     0.3101     0.6059   0.512    0.615  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 12.00707)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 216.13  on 18  degrees of freedom
## AIC: 128.8
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ TRBINJ_PRSNCE_Neu13 + sex + educ_years_std + 
##     P1_APOE4_load_std, family = gaussian(), data = delta_neuro_history)
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)          0.08396    1.42155   0.059    0.954
## TRBINJ_PRSNCE_Neu13 -3.09464    3.35508  -0.922    0.370
## sex                  1.01496    1.74723   0.581    0.569
## educ_years_std       0.47704    1.00508   0.475    0.641
## P1_APOE4_load_std   -0.25324    0.66783  -0.379    0.710
## 
## (Dispersion parameter for gaussian family taken to be 15.32214)
## 
##     Null deviance: 266.65  on 20  degrees of freedom
## Residual deviance: 245.15  on 16  degrees of freedom
##   (2 observations deleted due to missingness)
## AIC: 123.2
## 
## Number of Fisher Scoring iterations: 2
## [1] FALSE FALSE FALSE

CU A- V+

## 
## Call:
## glm(formula = delta ~ NEUROL_PRSNCE_Neu13, family = gaussian(), 
##     data = delta_neuro_history)
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)          0.41233    1.08085   0.381    0.710
## NEUROL_PRSNCE_Neu13 -0.06243    1.34806  -0.046    0.964
## 
## (Dispersion parameter for gaussian family taken to be 5.841179)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 70.094  on 12  degrees of freedom
## AIC: 68.281
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ CVADIS_PRSNCE_Neu13, family = gaussian(), 
##     data = delta_neuro_history)
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)           0.1710     0.6806   0.251    0.806
## CVADIS_PRSNCE_Neu13   1.4084     1.8007   0.782    0.449
## 
## (Dispersion parameter for gaussian family taken to be 5.558847)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 66.706  on 12  degrees of freedom
## AIC: 67.588
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ TRBINJ_PRSNCE_Neu13, family = gaussian(), 
##     data = delta_neuro_history)
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)           0.2351     0.7775   0.302    0.768
## TRBINJ_PRSNCE_Neu13   0.9754     1.6186   0.603    0.559
## 
## (Dispersion parameter for gaussian family taken to be 6.045545)
## 
##     Null deviance: 68.697  on 12  degrees of freedom
## Residual deviance: 66.501  on 11  degrees of freedom
##   (1 observation deleted due to missingness)
## AIC: 64.112
## 
## Number of Fisher Scoring iterations: 2

Takeaway

No significant associations found.

Sleep Variables

CU A- V-

## 
## Call:
## glm(formula = delta ~ PSQITT_C1TTLS_Nut901, family = gaussian(), 
##     data = delta_sleep)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)           0.05687    0.29936   0.190    0.850
## PSQITT_C1TTLS_Nut901  0.28632    0.30046   0.953    0.342
## 
## (Dispersion parameter for gaussian family taken to be 12.36745)
## 
##     Null deviance: 1693.2  on 137  degrees of freedom
## Residual deviance: 1682.0  on 136  degrees of freedom
##   (2 observations deleted due to missingness)
## AIC: 742.69
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ PSQITT_C5TTLS_Nut901, family = gaussian(), 
##     data = delta_sleep)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)           0.05687    0.29969   0.190    0.850
## PSQITT_C5TTLS_Nut901 -0.23532    0.30078  -0.782    0.435
## 
## (Dispersion parameter for gaussian family taken to be 12.39424)
## 
##     Null deviance: 1693.2  on 137  degrees of freedom
## Residual deviance: 1685.6  on 136  degrees of freedom
##   (2 observations deleted due to missingness)
## AIC: 742.99
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ PSQITT_PSQITT_Nut901, family = gaussian(), 
##     data = delta_sleep)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)           0.05687    0.29939   0.190    0.850
## PSQITT_PSQITT_Nut901  0.28299    0.30048   0.942    0.348
## 
## (Dispersion parameter for gaussian family taken to be 12.36935)
## 
##     Null deviance: 1693.2  on 137  degrees of freedom
## Residual deviance: 1682.2  on 136  degrees of freedom
##   (2 observations deleted due to missingness)
## AIC: 742.71
## 
## Number of Fisher Scoring iterations: 2

CU A+ V-

## 
## Call:
## glm(formula = delta ~ PSQITT_C1TTLS_Nut901, family = gaussian(), 
##     data = delta_sleep)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)            0.3349     0.7677   0.436    0.667
## PSQITT_C1TTLS_Nut901  -0.1378     0.7849  -0.176    0.862
## 
## (Dispersion parameter for gaussian family taken to be 13.55499)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 284.65  on 21  degrees of freedom
## AIC: 129.13
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ PSQITT_C5TTLS_Nut901, family = gaussian(), 
##     data = delta_sleep)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)            0.3349     0.7634   0.439    0.665
## PSQITT_C5TTLS_Nut901  -0.4021     0.7806  -0.515    0.612
## 
## (Dispersion parameter for gaussian family taken to be 13.40548)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 281.52  on 21  degrees of freedom
## AIC: 128.88
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ PSQITT_PSQITT_Nut901, family = gaussian(), 
##     data = delta_sleep)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)            0.3349     0.7606   0.440    0.664
## PSQITT_PSQITT_Nut901  -0.5080     0.7777  -0.653    0.521
## 
## (Dispersion parameter for gaussian family taken to be 13.30448)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 279.39  on 21  degrees of freedom
## AIC: 128.71
## 
## Number of Fisher Scoring iterations: 2

CU A- V+

## 
## Call:
## glm(formula = delta ~ PSQITT_C1TTLS_Nut901, family = gaussian(), 
##     data = delta_sleep)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            0.3722     0.5504   0.676   0.5117  
## PSQITT_C1TTLS_Nut901   1.2159     0.5711   2.129   0.0547 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 4.240484)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 50.886  on 12  degrees of freedom
## AIC: 63.798
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ PSQITT_C5TTLS_Nut901, family = gaussian(), 
##     data = delta_sleep)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            0.3722     0.5720   0.651    0.527  
## PSQITT_C5TTLS_Nut901   1.0795     0.5935   1.819    0.094 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 4.579834)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 54.958  on 12  degrees of freedom
## AIC: 64.875
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ PSQITT_PSQITT_Nut901, family = gaussian(), 
##     data = delta_sleep)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)            0.3722     0.5852   0.636    0.537
## PSQITT_PSQITT_Nut901   0.9837     0.6073   1.620    0.131
## 
## (Dispersion parameter for gaussian family taken to be 4.794019)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 57.528  on 12  degrees of freedom
## AIC: 65.515
## 
## Number of Fisher Scoring iterations: 2

Takeaway

No significant associations found.

MFE_ADCS_ADL

CU A- V-

## 
## Call:
## glm(formula = delta ~ P1_MFE_TotalScore, family = gaussian(), 
##     data = delta_mfe)
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)        0.07512    0.29683   0.253    0.801
## P1_MFE_TotalScore -0.12228    0.29790  -0.410    0.682
## 
## (Dispersion parameter for gaussian family taken to be 12.33549)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1702.3  on 138  degrees of freedom
## AIC: 753.04
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_ADLAUT_SCORE9_Neu213, family = gaussian(), 
##     data = delta_mfe)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)              0.07512    0.29655   0.253     0.80
## P1_ADLAUT_SCORE9_Neu213 -0.19651    0.29761  -0.660     0.51
## 
## (Dispersion parameter for gaussian family taken to be 12.31165)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1699.0  on 138  degrees of freedom
## AIC: 752.76
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_ADLINF_SCORE9_Neu214, family = gaussian(), 
##     data = delta_mfe)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.1029     0.2980   0.345    0.730
## P1_ADLINF_SCORE9_Neu214  -0.3285     0.2990  -1.098    0.274
## 
## (Dispersion parameter for gaussian family taken to be 12.1621)
## 
##     Null deviance: 1656.6  on 136  degrees of freedom
## Residual deviance: 1641.9  on 135  degrees of freedom
##   (3 observations deleted due to missingness)
## AIC: 735.04
## 
## Number of Fisher Scoring iterations: 2

CU A+ V-

## 
## Call:
## glm(formula = delta ~ P1_MFE_TotalScore, family = gaussian(), 
##     data = delta_mfe)
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)         0.3349     0.7499   0.447    0.660
## P1_MFE_TotalScore   0.7827     0.7667   1.021    0.319
## 
## (Dispersion parameter for gaussian family taken to be 12.93314)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 271.60  on 21  degrees of freedom
## AIC: 128.05
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_ADLAUT_SCORE9_Neu213, family = gaussian(), 
##     data = delta_mfe)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.3349     0.7641   0.438    0.666
## P1_ADLAUT_SCORE9_Neu213  -0.3733     0.7813  -0.478    0.638
## 
## (Dispersion parameter for gaussian family taken to be 13.42891)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 282.01  on 21  degrees of freedom
## AIC: 128.92
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_ADLINF_SCORE9_Neu214, family = gaussian(), 
##     data = delta_mfe)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.3387     0.7811   0.434    0.669
## P1_ADLINF_SCORE9_Neu214   0.8900     0.7994   1.113    0.279
## 
## (Dispersion parameter for gaussian family taken to be 13.42148)
## 
##     Null deviance: 285.07  on 21  degrees of freedom
## Residual deviance: 268.43  on 20  degrees of freedom
##   (1 observation deleted due to missingness)
## AIC: 123.47
## 
## Number of Fisher Scoring iterations: 2

CU A- V+

## 
## Call:
## glm(formula = delta ~ P1_MFE_TotalScore, family = gaussian(), 
##     data = delta_mfe)
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)
## (Intercept)         0.3722     0.6198   0.601    0.559
## P1_MFE_TotalScore   0.6550     0.6432   1.018    0.329
## 
## (Dispersion parameter for gaussian family taken to be 5.377434)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 64.529  on 12  degrees of freedom
## AIC: 67.123
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_ADLAUT_SCORE9_Neu213, family = gaussian(), 
##     data = delta_mfe)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.3722     0.5529   0.673   0.5136  
## P1_ADLAUT_SCORE9_Neu213  -1.2011     0.5737  -2.093   0.0582 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 4.27937)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 51.352  on 12  degrees of freedom
## AIC: 63.925
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_ADLINF_SCORE9_Neu214, family = gaussian(), 
##     data = delta_mfe)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.3722     0.6430   0.579    0.573
## P1_ADLINF_SCORE9_Neu214   0.2243     0.6672   0.336    0.743
## 
## (Dispersion parameter for gaussian family taken to be 5.787733)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 69.453  on 12  degrees of freedom
## AIC: 68.153
## 
## Number of Fisher Scoring iterations: 2

Takeaway

No significant associations found.

Cardiovascular Parameters

CU A- V-

## 
## Call:
## glm(formula = delta ~ CARDIO_PRSNCE_Neu12, family = gaussian(), 
##     data = delta_cardio)
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)           0.2706     0.3193   0.848    0.398
## CARDIO_PRSNCE_Neu12  -1.3032     0.8244  -1.581    0.116
## 
## (Dispersion parameter for gaussian family taken to be 12.13088)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1674.1  on 138  degrees of freedom
## AIC: 750.69
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ IHDISE_PRSNCE_Neu12, family = gaussian(), 
##     data = delta_cardio)
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           0.1513     0.2970   0.509   0.6113  
## IHDISE_PRSNCE_Neu12  -3.5529     2.0287  -1.751   0.0821 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 12.08201)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1667.3  on 138  degrees of freedom
## AIC: 750.13
## 
## Number of Fisher Scoring iterations: 2

CU A+ V-

## 
## Call:
## glm(formula = delta ~ CARDIO_PRSNCE_Neu12, family = gaussian(), 
##     data = delta_cardio)
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)           0.4352     0.9506   0.458    0.652
## CARDIO_PRSNCE_Neu12  -0.2883     1.6118  -0.179    0.860
## 
## (Dispersion parameter for gaussian family taken to be 13.55423)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 284.64  on 21  degrees of freedom
## AIC: 129.13
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ IHDISE_PRSNCE_Neu12, family = gaussian(), 
##     data = delta_cardio)
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)          0.33267    0.78552   0.424    0.676
## IHDISE_PRSNCE_Neu12  0.05139    3.76720   0.014    0.989
## 
## (Dispersion parameter for gaussian family taken to be 13.57477)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 285.07  on 21  degrees of freedom
## AIC: 129.17
## 
## Number of Fisher Scoring iterations: 2

CU A- V+

## 
## Call:
## glm(formula = delta ~ CARDIO_PRSNCE_Neu12, family = gaussian(), 
##     data = delta_cardio)
## 
## Coefficients: (1 not defined because of singularities)
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)           0.3722     0.6206     0.6    0.559
## CARDIO_PRSNCE_Neu12       NA         NA      NA       NA
## 
## (Dispersion parameter for gaussian family taken to be 5.392821)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 70.107  on 13  degrees of freedom
## AIC: 66.284
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ IHDISE_PRSNCE_Neu12, family = gaussian(), 
##     data = delta_cardio)
## 
## Coefficients: (1 not defined because of singularities)
##                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)           0.3722     0.6206     0.6    0.559
## IHDISE_PRSNCE_Neu12       NA         NA      NA       NA
## 
## (Dispersion parameter for gaussian family taken to be 5.392821)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 70.107  on 13  degrees of freedom
## AIC: 66.284
## 
## Number of Fisher Scoring iterations: 2

Takeaway

No significant associations found in the sample.

Neurobehavioral Parameters

CU A- V-

## 
## Call:
## glm(formula = delta ~ BPSHAD_HADANX_Neu207, family = gaussian(), 
##     data = delta_neurobehav)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)           0.07512    0.29667   0.253    0.800
## BPSHAD_HADANX_Neu207  0.16976    0.29773   0.570    0.569
## 
## (Dispersion parameter for gaussian family taken to be 12.32152)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1700.4  on 138  degrees of freedom
## AIC: 752.88
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ BPSHAD_HADEPR_Neu207, family = gaussian(), 
##     data = delta_neurobehav)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)           0.07512    0.29701   0.253    0.801
## BPSHAD_HADEPR_Neu207  0.02026    0.29808   0.068    0.946
## 
## (Dispersion parameter for gaussian family taken to be 12.35013)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1704.3  on 138  degrees of freedom
## AIC: 753.2
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ BPSNPI_NPITOT_Neu209, family = gaussian(), 
##     data = delta_neurobehav)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)            0.1029     0.2988   0.344    0.731
## BPSNPI_NPITOT_Neu209   0.1958     0.2999   0.653    0.515
## 
## (Dispersion parameter for gaussian family taken to be 12.23218)
## 
##     Null deviance: 1656.6  on 136  degrees of freedom
## Residual deviance: 1651.3  on 135  degrees of freedom
##   (3 observations deleted due to missingness)
## AIC: 735.83
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ BPSTRS_PSQZSC_Neu210, family = gaussian(), 
##     data = delta_neurobehav)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)           0.12265    0.29525   0.415    0.678
## BPSTRS_PSQZSC_Neu210  0.05455    0.29631   0.184    0.854
## 
## (Dispersion parameter for gaussian family taken to be 12.11671)
## 
##     Null deviance: 1660.4  on 138  degrees of freedom
## Residual deviance: 1660.0  on 137  degrees of freedom
##   (1 observation deleted due to missingness)
## AIC: 745.2
## 
## Number of Fisher Scoring iterations: 2

CU A+ V-

## 
## Call:
## glm(formula = delta ~ BPSHAD_HADANX_Neu207, family = gaussian(), 
##     data = delta_neurobehav)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)            0.3349     0.7665   0.437    0.667
## BPSHAD_HADANX_Neu207   0.2411     0.7838   0.308    0.761
## 
## (Dispersion parameter for gaussian family taken to be 13.51398)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 283.79  on 21  degrees of freedom
## AIC: 129.06
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ BPSHAD_HADEPR_Neu207, family = gaussian(), 
##     data = delta_neurobehav)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)            0.3349     0.7647   0.438    0.666
## BPSHAD_HADEPR_Neu207   0.3463     0.7819   0.443    0.662
## 
## (Dispersion parameter for gaussian family taken to be 13.44923)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 282.43  on 21  degrees of freedom
## AIC: 128.95
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ BPSNPI_NPITOT_Neu209, family = gaussian(), 
##     data = delta_neurobehav)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)            0.1490     0.7772   0.192    0.850
## BPSNPI_NPITOT_Neu209   1.1708     0.7964   1.470    0.158
## 
## (Dispersion parameter for gaussian family taken to be 12.68583)
## 
##     Null deviance: 268.45  on 20  degrees of freedom
## Residual deviance: 241.03  on 19  degrees of freedom
##   (2 observations deleted due to missingness)
## AIC: 116.84
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ BPSTRS_PSQZSC_Neu210, family = gaussian(), 
##     data = delta_neurobehav)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)            0.3349     0.7517   0.446    0.661
## BPSTRS_PSQZSC_Neu210  -0.7424     0.7686  -0.966    0.345
## 
## (Dispersion parameter for gaussian family taken to be 12.99754)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 272.95  on 21  degrees of freedom
## AIC: 128.17
## 
## Number of Fisher Scoring iterations: 2

CU A- V+

## 
## Call:
## glm(formula = delta ~ BPSHAD_HADANX_Neu207, family = gaussian(), 
##     data = delta_neurobehav)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)            0.3722     0.6192   0.601    0.559
## BPSHAD_HADANX_Neu207   0.6618     0.6426   1.030    0.323
## 
## (Dispersion parameter for gaussian family taken to be 5.367691)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 64.412  on 12  degrees of freedom
## AIC: 67.098
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ BPSHAD_HADEPR_Neu207, family = gaussian(), 
##     data = delta_neurobehav)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)            0.3722     0.5892   0.632    0.539
## BPSHAD_HADEPR_Neu207   0.9520     0.6114   1.557    0.145
## 
## (Dispersion parameter for gaussian family taken to be 4.860293)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 58.324  on 12  degrees of freedom
## AIC: 65.708
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ BPSNPI_NPITOT_Neu209, family = gaussian(), 
##     data = delta_neurobehav)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            0.3722     0.5657   0.658   0.5230  
## BPSNPI_NPITOT_Neu209   1.1213     0.5870   1.910   0.0803 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 4.480152)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 53.762  on 12  degrees of freedom
## AIC: 64.567
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ BPSTRS_PSQZSC_Neu210, family = gaussian(), 
##     data = delta_neurobehav)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)            0.3722     0.5672   0.656   0.5241  
## BPSTRS_PSQZSC_Neu210  -1.1111     0.5887  -1.888   0.0835 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 4.504759)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 54.057  on 12  degrees of freedom
## AIC: 64.644
## 
## Number of Fisher Scoring iterations: 2

Takeway

No significant associations found.

Vascular Parameters

CU A- V-

## 
## Call:
## glm(formula = delta ~ P1_CVMSRS_SYSTBP_Nur201, family = gaussian(), 
##     data = delta_vascular)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)              0.07512    0.29613   0.254    0.800
## P1_CVMSRS_SYSTBP_Nur201 -0.27055    0.29719  -0.910    0.364
## 
## (Dispersion parameter for gaussian family taken to be 12.27682)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1694.2  on 138  degrees of freedom
## AIC: 752.37
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_CVMSRS_DIASBP_Nur201, family = gaussian(), 
##     data = delta_vascular)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)              0.07512    0.29621   0.254    0.800
## P1_CVMSRS_DIASBP_Nur201 -0.25834    0.29727  -0.869    0.386
## 
## (Dispersion parameter for gaussian family taken to be 12.28333)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1695.1  on 138  degrees of freedom
## AIC: 752.44
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_CVMSRS_HRTRTE_Nur201, family = gaussian(), 
##     data = delta_vascular)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)  
## (Intercept)              0.07512    0.29372   0.256   0.7985  
## P1_CVMSRS_HRTRTE_Nur201  0.52027    0.29477   1.765   0.0798 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 12.07791)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1666.8  on 138  degrees of freedom
## AIC: 750.08
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_CVMSRS_RTANKA_Nur201, family = gaussian(), 
##     data = delta_vascular)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)              0.07512    0.29641   0.253    0.800
## P1_CVMSRS_RTANKA_Nur201  0.22385    0.29747   0.752    0.453
## 
## (Dispersion parameter for gaussian family taken to be 12.30008)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1697.4  on 138  degrees of freedom
## AIC: 752.63
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_CVMSRS_LTANKA_Nur201, family = gaussian(), 
##     data = delta_vascular)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)              0.07512    0.29473   0.255    0.799
## P1_CVMSRS_LTANKA_Nur201 -0.43353    0.29579  -1.466    0.145
## 
## (Dispersion parameter for gaussian family taken to be 12.16123)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1678.3  on 138  degrees of freedom
## AIC: 751.04
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_ECG999_HRTRTE_Nur202, family = gaussian(), 
##     data = delta_vascular)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)              0.07512    0.29667   0.253    0.800
## P1_ECG999_HRTRTE_Nur202  0.16843    0.29774   0.566    0.573
## 
## (Dispersion parameter for gaussian family taken to be 12.32197)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1700.4  on 138  degrees of freedom
## AIC: 752.88
## 
## Number of Fisher Scoring iterations: 2

CU A+ V-

## 
## Call:
## glm(formula = delta ~ P1_CVMSRS_SYSTBP_Nur201, family = gaussian(), 
##     data = delta_vascular)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.3349     0.7673   0.436    0.667
## P1_CVMSRS_SYSTBP_Nur201   0.1775     0.7846   0.226    0.823
## 
## (Dispersion parameter for gaussian family taken to be 13.54187)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 284.38  on 21  degrees of freedom
## AIC: 129.11
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_CVMSRS_DIASBP_Nur201, family = gaussian(), 
##     data = delta_vascular)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.3349     0.7522   0.445    0.661
## P1_CVMSRS_DIASBP_Nur201  -0.7323     0.7691  -0.952    0.352
## 
## (Dispersion parameter for gaussian family taken to be 13.01306)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 273.27  on 21  degrees of freedom
## AIC: 128.2
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_CVMSRS_HRTRTE_Nur201, family = gaussian(), 
##     data = delta_vascular)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.3349     0.7495   0.447    0.660
## P1_CVMSRS_HRTRTE_Nur201  -0.7904     0.7664  -1.031    0.314
## 
## (Dispersion parameter for gaussian family taken to be 12.92049)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 271.33  on 21  degrees of freedom
## AIC: 128.03
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_CVMSRS_RTANKA_Nur201, family = gaussian(), 
##     data = delta_vascular)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.3349     0.7216   0.464    0.647
## P1_CVMSRS_RTANKA_Nur201  -1.2353     0.7378  -1.674    0.109
## 
## (Dispersion parameter for gaussian family taken to be 11.9763)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 251.50  on 21  degrees of freedom
## AIC: 126.29
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_CVMSRS_LTANKA_Nur201, family = gaussian(), 
##     data = delta_vascular)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.3349     0.7581   0.442    0.663
## P1_CVMSRS_LTANKA_Nur201  -0.5821     0.7752  -0.751    0.461
## 
## (Dispersion parameter for gaussian family taken to be 13.21997)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 277.62  on 21  degrees of freedom
## AIC: 128.56
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_ECG999_HRTRTE_Nur202, family = gaussian(), 
##     data = delta_vascular)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.0546     0.7150   0.076    0.940
## P1_ECG999_HRTRTE_Nur202  -0.9857     0.7318  -1.347    0.193
## 
## (Dispersion parameter for gaussian family taken to be 11.2457)
## 
##     Null deviance: 245.32  on 21  degrees of freedom
## Residual deviance: 224.91  on 20  degrees of freedom
##   (1 observation deleted due to missingness)
## AIC: 119.58
## 
## Number of Fisher Scoring iterations: 2

CU A- V+

## 
## Call:
## glm(formula = delta ~ P1_CVMSRS_SYSTBP_Nur201, family = gaussian(), 
##     data = delta_vascular)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.3722     0.6452   0.577    0.575
## P1_CVMSRS_SYSTBP_Nur201   0.1144     0.6696   0.171    0.867
## 
## (Dispersion parameter for gaussian family taken to be 5.828034)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 69.936  on 12  degrees of freedom
## AIC: 68.25
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_CVMSRS_DIASBP_Nur201, family = gaussian(), 
##     data = delta_vascular)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.3722     0.6345   0.587    0.568
## P1_CVMSRS_DIASBP_Nur201   0.4358     0.6585   0.662    0.521
## 
## (Dispersion parameter for gaussian family taken to be 5.63652)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 67.638  on 12  degrees of freedom
## AIC: 67.782
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_CVMSRS_HRTRTE_Nur201, family = gaussian(), 
##     data = delta_vascular)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.3722     0.6447   0.577    0.574
## P1_CVMSRS_HRTRTE_Nur201  -0.1452     0.6691  -0.217    0.832
## 
## (Dispersion parameter for gaussian family taken to be 5.819384)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 69.833  on 12  degrees of freedom
## AIC: 68.229
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_CVMSRS_RTANKA_Nur201, family = gaussian(), 
##     data = delta_vascular)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.3722     0.6297   0.591    0.565
## P1_CVMSRS_RTANKA_Nur201  -0.5188     0.6534  -0.794    0.443
## 
## (Dispersion parameter for gaussian family taken to be 5.550613)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 66.607  on 12  degrees of freedom
## AIC: 67.567
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_CVMSRS_LTANKA_Nur201, family = gaussian(), 
##     data = delta_vascular)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.3722     0.6206     0.6    0.560
## P1_CVMSRS_LTANKA_Nur201  -0.6442     0.6441    -1.0    0.337
## 
## (Dispersion parameter for gaussian family taken to be 5.392701)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 64.712  on 12  degrees of freedom
## AIC: 67.163
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_ECG999_HRTRTE_Nur202, family = gaussian(), 
##     data = delta_vascular)
## 
## Coefficients:
##                         Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.3722     0.6090   0.611    0.553
## P1_ECG999_HRTRTE_Nur202  -0.7742     0.6320  -1.225    0.244
## 
## (Dispersion parameter for gaussian family taken to be 5.192883)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 62.315  on 12  degrees of freedom
## AIC: 66.634
## 
## Number of Fisher Scoring iterations: 2

Takeaway

No significant associations found.

Cognitive Reserve

CU A- V-

## 
## Call:
## glm(formula = delta ~ CRC_Total_std, family = gaussian(), data = delta_cog_reserv)
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)    0.07772    0.32907   0.236    0.814
## CRC_Total_std  0.26842    0.33060   0.812    0.419
## 
## (Dispersion parameter for gaussian family taken to be 11.69501)
## 
##     Null deviance: 1247.4  on 107  degrees of freedom
## Residual deviance: 1239.7  on 106  degrees of freedom
##   (32 observations deleted due to missingness)
## AIC: 576.06
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_VOCBWS_TTLZSC_Nps102_std, family = gaussian(), 
##     data = delta_cog_reserv)
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                  0.06107    0.29109   0.210  0.83414   
## P1_VOCBWS_TTLZSC_Nps102_std -0.79376    0.29214  -2.717  0.00744 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 11.77801)
## 
##     Null deviance: 1700.5  on 138  degrees of freedom
## Residual deviance: 1613.6  on 137  degrees of freedom
##   (1 observation deleted due to missingness)
## AIC: 741.26
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_BILINGUISMO_std, family = gaussian(), 
##     data = delta_cog_reserv)
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)         0.07512    0.29700   0.253    0.801
## P1_BILINGUISMO_std  0.03473    0.29807   0.117    0.907
## 
## (Dispersion parameter for gaussian family taken to be 12.34933)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1704.2  on 138  degrees of freedom
## AIC: 753.19
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_CUEST_OCIO_PRODUCTIVAS_TOTAL_std, family = gaussian(), 
##     data = delta_cog_reserv)
## 
## Coefficients:
##                                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)                          0.07512    0.29698   0.253    0.801
## P1_CUEST_OCIO_PRODUCTIVAS_TOTAL_std -0.05419    0.29805  -0.182    0.856
## 
## (Dispersion parameter for gaussian family taken to be 12.34759)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1704.0  on 138  degrees of freedom
## AIC: 753.17
## 
## Number of Fisher Scoring iterations: 2

CU A- V- correcting for sex, educ_years, and APOE4 load

## 
## Call:
## glm(formula = delta ~ CRC_Total_std + sex + educ_years_std + 
##     P1_APOE4_load_std, family = gaussian(), data = delta_cog_reserv)
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        -0.1355     0.5042  -0.269  0.78866   
## CRC_Total_std      -0.4904     0.4156  -1.180  0.24071   
## sex                 0.2830     0.6623   0.427  0.67004   
## educ_years_std      1.1990     0.4054   2.957  0.00385 **
## P1_APOE4_load_std   0.1420     0.3524   0.403  0.68783   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 11.04597)
## 
##     Null deviance: 1247.4  on 107  degrees of freedom
## Residual deviance: 1137.7  on 103  degrees of freedom
##   (32 observations deleted due to missingness)
## AIC: 572.79
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_VOCBWS_TTLZSC_Nps102_std + sex + educ_years_std + 
##     P1_APOE4_load_std, family = gaussian(), data = delta_cog_reserv)
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                  0.35501    0.42714   0.831 0.407382    
## P1_VOCBWS_TTLZSC_Nps102_std -1.39063    0.31992  -4.347 2.71e-05 ***
## sex                         -0.49802    0.57490  -0.866 0.387888    
## educ_years_std               1.22090    0.31462   3.881 0.000163 ***
## P1_APOE4_load_std            0.03967    0.30707   0.129 0.897400    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 10.78994)
## 
##     Null deviance: 1700.5  on 138  degrees of freedom
## Residual deviance: 1445.9  on 134  degrees of freedom
##   (1 observation deleted due to missingness)
## AIC: 732
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_BILINGUISMO_std + sex + educ_years_std + 
##     P1_APOE4_load_std, family = gaussian(), data = delta_cog_reserv)
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)         0.05437    0.45011   0.121   0.9040  
## P1_BILINGUISMO_std -0.01593    0.30039  -0.053   0.9578  
## sex                 0.04092    0.59904   0.068   0.9456  
## educ_years_std      0.62058    0.29720   2.088   0.0387 *
## P1_APOE4_load_std   0.04586    0.32810   0.140   0.8890  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 12.22613)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1650.5  on 135  degrees of freedom
## AIC: 754.71
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_CUEST_OCIO_PRODUCTIVAS_TOTAL_std + sex + 
##     educ_years_std + P1_APOE4_load_std, family = gaussian(), 
##     data = delta_cog_reserv)
## 
## Coefficients:
##                                     Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                          0.05581    0.44769   0.125   0.9010  
## P1_CUEST_OCIO_PRODUCTIVAS_TOTAL_std -0.25130    0.30858  -0.814   0.4169  
## sex                                  0.03925    0.59500   0.066   0.9475  
## educ_years_std                       0.69019    0.30789   2.242   0.0266 *
## P1_APOE4_load_std                    0.05338    0.32583   0.164   0.8701  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 12.16662)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1642.5  on 135  degrees of freedom
## AIC: 754.03
## 
## Number of Fisher Scoring iterations: 2
## [1] 0.4814278103 0.0001084402 0.9577967919 0.5558294353

See the association between years of education and vocabulary.

See the association between delta and vocabulary.

CU A+ V-

## 
## Call:
## glm(formula = delta ~ CRC_Total_std, family = gaussian(), data = delta_cog_reserv)
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)     0.7824     0.7402   1.057    0.305
## CRC_Total_std  -1.2171     0.7605  -1.601    0.128
## 
## (Dispersion parameter for gaussian family taken to be 10.40935)
## 
##     Null deviance: 203.62  on 18  degrees of freedom
## Residual deviance: 176.96  on 17  degrees of freedom
##   (4 observations deleted due to missingness)
## AIC: 102.32
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_VOCBWS_TTLZSC_Nps102_std, family = gaussian(), 
##     data = delta_cog_reserv)
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                   0.3349     0.6956   0.481   0.6352  
## P1_VOCBWS_TTLZSC_Nps102_std  -1.5283     0.7112  -2.149   0.0435 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 11.12785)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 233.68  on 21  degrees of freedom
## AIC: 124.6
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_BILINGUISMO_std, family = gaussian(), 
##     data = delta_cog_reserv)
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)          0.3349     0.7096   0.472    0.642  
## P1_BILINGUISMO_std  -1.3797     0.7255  -1.902    0.071 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 11.5808)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 243.20  on 21  degrees of freedom
## AIC: 125.51
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_CUEST_OCIO_PRODUCTIVAS_TOTAL_std, family = gaussian(), 
##     data = delta_cog_reserv)
## 
## Coefficients:
##                                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)                           0.3349     0.7396   0.453    0.655
## P1_CUEST_OCIO_PRODUCTIVAS_TOTAL_std  -0.9734     0.7563  -1.287    0.212
## 
## (Dispersion parameter for gaussian family taken to be 12.58221)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 264.23  on 21  degrees of freedom
## AIC: 127.42
## 
## Number of Fisher Scoring iterations: 2

CU A+ V- correcting for sex, educ_years, and APOE4 load

## 
## Call:
## glm(formula = delta ~ CRC_Total_std + sex + educ_years_std + 
##     P1_APOE4_load_std, family = gaussian(), data = delta_cog_reserv)
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)  
## (Intercept)        1.18668    1.16257   1.021   0.3247  
## CRC_Total_std     -2.46616    0.94911  -2.598   0.0210 *
## sex                0.26870    1.48633   0.181   0.8591  
## educ_years_std     2.24922    1.02008   2.205   0.0447 *
## P1_APOE4_load_std -0.07155    0.58923  -0.121   0.9051  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 9.379471)
## 
##     Null deviance: 203.62  on 18  degrees of freedom
## Residual deviance: 131.31  on 14  degrees of freedom
##   (4 observations deleted due to missingness)
## AIC: 102.65
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_VOCBWS_TTLZSC_Nps102_std + sex + educ_years_std + 
##     P1_APOE4_load_std, family = gaussian(), data = delta_cog_reserv)
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                   1.2035     1.2152   0.990   0.3351  
## P1_VOCBWS_TTLZSC_Nps102_std  -2.0822     0.8676  -2.400   0.0274 *
## sex                          -0.9060     1.6101  -0.563   0.5806  
## educ_years_std                0.8935     0.8203   1.089   0.2904  
## P1_APOE4_load_std            -0.4893     0.6023  -0.812   0.4272  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 11.90155)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 214.23  on 18  degrees of freedom
## AIC: 128.6
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_BILINGUISMO_std + sex + educ_years_std + 
##     P1_APOE4_load_std, family = gaussian(), data = delta_cog_reserv)
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)  
## (Intercept)         0.29237    1.20108   0.243    0.810  
## P1_BILINGUISMO_std -1.45818    0.80545  -1.810    0.087 .
## sex                 0.26439    1.58738   0.167    0.870  
## educ_years_std      0.43071    0.81480   0.529    0.604  
## P1_APOE4_load_std  -0.02726    0.61276  -0.044    0.965  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 13.29007)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 239.22  on 18  degrees of freedom
## AIC: 131.13
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_CUEST_OCIO_PRODUCTIVAS_TOTAL_std + sex + 
##     educ_years_std + P1_APOE4_load_std, family = gaussian(), 
##     data = delta_cog_reserv)
## 
## Coefficients:
##                                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)                         -0.06812    1.23763  -0.055    0.957
## P1_CUEST_OCIO_PRODUCTIVAS_TOTAL_std -1.36417    0.90734  -1.503    0.150
## sex                                  1.09978    1.66522   0.660    0.517
## educ_years_std                       0.72413    0.91144   0.794    0.437
## P1_APOE4_load_std                   -0.01583    0.62890  -0.025    0.980
## 
## (Dispersion parameter for gaussian family taken to be 13.95723)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 251.23  on 18  degrees of freedom
## AIC: 132.26
## 
## Number of Fisher Scoring iterations: 2
## [1] 0.05485583 0.05485583 0.11594744 0.15005737

CU A- V+

## 
## Call:
## glm(formula = delta ~ CRC_Total_std, family = gaussian(), data = delta_cog_reserv)
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)
## (Intercept)     0.6576     0.6069   1.084    0.307
## CRC_Total_std  -1.0081     0.6366  -1.584    0.148
## 
## (Dispersion parameter for gaussian family taken to be 4.052105)
## 
##     Null deviance: 46.631  on 10  degrees of freedom
## Residual deviance: 36.469  on  9  degrees of freedom
##   (3 observations deleted due to missingness)
## AIC: 50.401
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_VOCBWS_TTLZSC_Nps102_std, family = gaussian(), 
##     data = delta_cog_reserv)
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)
## (Intercept)                  0.37219    0.64586   0.576    0.575
## P1_VOCBWS_TTLZSC_Nps102_std -0.04584    0.67024  -0.068    0.947
## 
## (Dispersion parameter for gaussian family taken to be 5.839946)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 70.079  on 12  degrees of freedom
## AIC: 68.278
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_BILINGUISMO_std, family = gaussian(), 
##     data = delta_cog_reserv)
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)
## (Intercept)          0.3722     0.6363   0.585    0.569
## P1_BILINGUISMO_std  -0.3998     0.6604  -0.605    0.556
## 
## (Dispersion parameter for gaussian family taken to be 5.669086)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 68.029  on 12  degrees of freedom
## AIC: 67.863
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ P1_CUEST_OCIO_PRODUCTIVAS_TOTAL_std, family = gaussian(), 
##     data = delta_cog_reserv)
## 
## Coefficients:
##                                     Estimate Std. Error t value Pr(>|t|)
## (Intercept)                           0.3722     0.5865   0.635    0.538
## P1_CUEST_OCIO_PRODUCTIVAS_TOTAL_std  -0.9731     0.6087  -1.599    0.136
## 
## (Dispersion parameter for gaussian family taken to be 4.816348)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 57.796  on 12  degrees of freedom
## AIC: 65.58
## 
## Number of Fisher Scoring iterations: 2

Takeaway

We see a strong association between delta and the vocabulary test Z score (P1_VOCBWS_TTLZSC_Nps102), when asking ā€œWhat is the effect of vocabulary independent of education, sex, and APOE4 load?ā€. The vocabulary test is highly correlated to the years of education (they share variance), but because we corrected for this effect in our question, we are looking at the effect of residual variation in the vocabulary that education does not explain. The vocabulary score predicts delta even after removing the shared variance with education, so vocabulary captures something beyond educational effects.

Tobacco

CU A- V-

## 
## Call:
## glm(formula = delta ~ SMOKNG_NUMCIG_Nut501_std, family = gaussian(), 
##     data = delta_tobacco)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.07512    0.29676   0.253    0.801
## SMOKNG_NUMCIG_Nut501_std -0.14577    0.29782  -0.489    0.625
## 
## (Dispersion parameter for gaussian family taken to be 12.32915)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1701.4  on 138  degrees of freedom
## AIC: 752.96
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SMOKNG_TYPSMK_Nut501, family = gaussian(), 
##     data = delta_tobacco)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)            0.3193     0.5292   0.603    0.547
## SMOKNG_TYPSMK_Nut501  -0.3562     0.6391  -0.557    0.578
## 
## (Dispersion parameter for gaussian family taken to be 12.32281)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1700.5  on 138  degrees of freedom
## AIC: 752.89
## 
## Number of Fisher Scoring iterations: 2

CU A+ V-

## 
## Call:
## glm(formula = delta ~ SMOKNG_NUMCIG_Nut501_std, family = gaussian(), 
##     data = delta_tobacco)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                0.3349     0.7344   0.456    0.653
## SMOKNG_NUMCIG_Nut501_std   1.0572     0.7509   1.408    0.174
## 
## (Dispersion parameter for gaussian family taken to be 12.40394)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 260.48  on 21  degrees of freedom
## AIC: 127.09
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SMOKNG_TYPSMK_Nut501, family = gaussian(), 
##     data = delta_tobacco)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)           -0.5669     1.2796  -0.443    0.662
## SMOKNG_TYPSMK_Nut501   1.3828     1.5845   0.873    0.393
## 
## (Dispersion parameter for gaussian family taken to be 13.0998)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 275.10  on 21  degrees of freedom
## AIC: 128.35
## 
## Number of Fisher Scoring iterations: 2

CU A- V+

## 
## Call:
## glm(formula = delta ~ SMOKNG_NUMCIG_Nut501_std, family = gaussian(), 
##     data = delta_tobacco)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                0.3722     0.6015   0.619    0.548
## SMOKNG_NUMCIG_Nut501_std   0.8469     0.6242   1.357    0.200
## 
## (Dispersion parameter for gaussian family taken to be 5.065207)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 60.782  on 12  degrees of freedom
## AIC: 66.286
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SMOKNG_TYPSMK_Nut501, family = gaussian(), 
##     data = delta_tobacco)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           -0.8284     0.8738  -0.948   0.3618  
## SMOKNG_TYPSMK_Nut501   2.1010     1.1559   1.818   0.0942 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 4.581073)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 54.973  on 12  degrees of freedom
## AIC: 64.879
## 
## Number of Fisher Scoring iterations: 2

Takeaway

No statistically significant associations found.

Alcohol

CU A- V-

## 
## Call:
## glm(formula = delta ~ ALCOHL_TOTALS_Nut401, family = gaussian(), 
##     data = delta_alcohol)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)            0.2654     0.3903    0.68    0.498
## ALCOHL_TOTALS_Nut401  -0.1360     0.1815   -0.75    0.455
## 
## (Dispersion parameter for gaussian family taken to be 12.30047)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1697.5  on 138  degrees of freedom
## AIC: 752.64
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ ALCH30_TOTALS_Nut401, family = gaussian(), 
##     data = delta_alcohol)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)            0.3271     0.3822   0.856    0.394
## ALCH30_TOTALS_Nut401  -0.1433     0.1376  -1.041    0.300
## 
## (Dispersion parameter for gaussian family taken to be 12.25425)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1691.1  on 138  degrees of freedom
## AIC: 752.11
## 
## Number of Fisher Scoring iterations: 2

CU A+ V-

## 
## Call:
## glm(formula = delta ~ ALCOHL_TOTALS_Nut401, family = gaussian(), 
##     data = delta_alcohol)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           -1.0194     0.9923  -1.027   0.3160  
## ALCOHL_TOTALS_Nut401   0.7395     0.3801   1.946   0.0652 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 11.50177)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 241.54  on 21  degrees of freedom
## AIC: 125.36
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ ALCH30_TOTALS_Nut401, family = gaussian(), 
##     data = delta_alcohol)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)
## (Intercept)           -0.3123     0.8324  -0.375    0.711
## ALCH30_TOTALS_Nut401   0.2462     0.1550   1.588    0.127
## 
## (Dispersion parameter for gaussian family taken to be 12.11892)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 254.50  on 21  degrees of freedom
## AIC: 126.56
## 
## Number of Fisher Scoring iterations: 2

CU A- V+

## 
## Call:
## glm(formula = delta ~ ALCOHL_TOTALS_Nut401, family = gaussian(), 
##     data = delta_alcohol)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           -1.0070     0.9584  -1.051   0.3141  
## ALCOHL_TOTALS_Nut401   0.7213     0.4016   1.796   0.0977 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 4.604367)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 55.252  on 12  degrees of freedom
## AIC: 64.95
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ ALCH30_TOTALS_Nut401, family = gaussian(), 
##     data = delta_alcohol)
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)           -0.8025     0.7680  -1.045   0.3167  
## ALCH30_TOTALS_Nut401   0.3453     0.1585   2.178   0.0501 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 4.186803)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 50.242  on 12  degrees of freedom
## AIC: 63.619
## 
## Number of Fisher Scoring iterations: 2

Takeaway

No significant associations found.

Diet

CU A- V-

## 
## Call:
## glm(formula = delta ~ MDAS13_NA13SC_Nut151_std, family = gaussian(), 
##     data = delta_diet)
## 
## Coefficients:
##                           Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.073462   0.303212   0.242    0.809
## MDAS13_NA13SC_Nut151_std -0.007234   0.304325  -0.024    0.981
## 
## (Dispersion parameter for gaussian family taken to be 12.59545)
## 
##     Null deviance: 1700.4  on 136  degrees of freedom
## Residual deviance: 1700.4  on 135  degrees of freedom
##   (3 observations deleted due to missingness)
## AIC: 739.84
## 
## Number of Fisher Scoring iterations: 2

CU A+ V-

## 
## Call:
## glm(formula = delta ~ MDAS13_NA13SC_Nut151_std, family = gaussian(), 
##     data = delta_diet)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                0.3349     0.7393   0.453    0.655
## MDAS13_NA13SC_Nut151_std  -0.9786     0.7559  -1.295    0.210
## 
## (Dispersion parameter for gaussian family taken to be 12.57159)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 264.00  on 21  degrees of freedom
## AIC: 127.4
## 
## Number of Fisher Scoring iterations: 2

CU A- V+

## 
## Call:
## glm(formula = delta ~ MDAS13_NA13SC_Nut151_std, family = gaussian(), 
##     data = delta_diet)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                0.3722     0.6440   0.578    0.574
## MDAS13_NA13SC_Nut151_std  -0.1826     0.6683  -0.273    0.789
## 
## (Dispersion parameter for gaussian family taken to be 5.806113)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 69.673  on 12  degrees of freedom
## AIC: 68.197
## 
## Number of Fisher Scoring iterations: 2

Takeaway

No significant associations found.

IPAQ & Sitting

CU A- V-

## 
## Call:
## glm(formula = delta ~ P1_IPAQMH_PATOTL_Nut701_std, family = gaussian(), 
##     data = delta_ipaq_sitting)
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)
## (Intercept)                  0.07512    0.29603   0.254    0.800
## P1_IPAQMH_PATOTL_Nut701_std  0.28557    0.29709   0.961    0.338
## 
## (Dispersion parameter for gaussian family taken to be 12.26841)
## 
##     Null deviance: 1704.4  on 139  degrees of freedom
## Residual deviance: 1693.0  on 138  degrees of freedom
## AIC: 752.27
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SDNTMF_CMPUTR_Nut801_std, family = gaussian(), 
##     data = delta_ipaq_sitting)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.05171    0.29745   0.174    0.862
## SDNTMF_CMPUTR_Nut801_std -0.25407    0.29852  -0.851    0.396
## 
## (Dispersion parameter for gaussian family taken to be 12.29786)
## 
##     Null deviance: 1693.7  on 138  degrees of freedom
## Residual deviance: 1684.8  on 137  degrees of freedom
##   (1 observation deleted due to missingness)
## AIC: 747.26
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SDNTMF_DRIVNG_Nut801_std, family = gaussian(), 
##     data = delta_ipaq_sitting)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.05171    0.29822   0.173    0.863
## SDNTMF_DRIVNG_Nut801_std  0.02273    0.29930   0.076    0.940
## 
## (Dispersion parameter for gaussian family taken to be 12.36236)
## 
##     Null deviance: 1693.7  on 138  degrees of freedom
## Residual deviance: 1693.6  on 137  degrees of freedom
##   (1 observation deleted due to missingness)
## AIC: 747.99
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SDNTSS_CMPUTR_Nut801_std, family = gaussian(), 
##     data = delta_ipaq_sitting)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.05171    0.29822   0.173    0.863
## SDNTSS_CMPUTR_Nut801_std  0.02864    0.29930   0.096    0.924
## 
## (Dispersion parameter for gaussian family taken to be 12.36205)
## 
##     Null deviance: 1693.7  on 138  degrees of freedom
## Residual deviance: 1693.6  on 137  degrees of freedom
##   (1 observation deleted due to missingness)
## AIC: 747.98
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SDNTSS_DRIVNG_Nut801_std, family = gaussian(), 
##     data = delta_ipaq_sitting)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.05171    0.29799   0.174    0.862
## SDNTSS_DRIVNG_Nut801_std -0.13983    0.29907  -0.468    0.641
## 
## (Dispersion parameter for gaussian family taken to be 12.34319)
## 
##     Null deviance: 1693.7  on 138  degrees of freedom
## Residual deviance: 1691.0  on 137  degrees of freedom
##   (1 observation deleted due to missingness)
## AIC: 747.77
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SDNTTT_SITTOT_Nut801_std, family = gaussian(), 
##     data = delta_ipaq_sitting)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.05171    0.29782   0.174    0.862
## SDNTTT_SITTOT_Nut801_std -0.18346    0.29890  -0.614    0.540
## 
## (Dispersion parameter for gaussian family taken to be 12.32898)
## 
##     Null deviance: 1693.7  on 138  degrees of freedom
## Residual deviance: 1689.1  on 137  degrees of freedom
##   (1 observation deleted due to missingness)
## AIC: 747.61
## 
## Number of Fisher Scoring iterations: 2

CU A+ V-

## 
## Call:
## glm(formula = delta ~ P1_IPAQMH_PATOTL_Nut701_std, family = gaussian(), 
##     data = delta_ipaq_sitting)
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)
## (Intercept)                  0.33490    0.76823   0.436    0.667
## P1_IPAQMH_PATOTL_Nut701_std -0.02642    0.78550  -0.034    0.973
## 
## (Dispersion parameter for gaussian family taken to be 13.57416)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 285.06  on 21  degrees of freedom
## AIC: 129.17
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SDNTMF_CMPUTR_Nut801_std, family = gaussian(), 
##     data = delta_ipaq_sitting)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                0.3349     0.7001   0.478   0.6373  
## SDNTMF_CMPUTR_Nut801_std  -1.4822     0.7158  -2.071   0.0509 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 11.27347)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 236.74  on 21  degrees of freedom
## AIC: 124.9
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SDNTMF_DRIVNG_Nut801_std, family = gaussian(), 
##     data = delta_ipaq_sitting)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                0.3349     0.7656   0.437    0.666
## SDNTMF_DRIVNG_Nut801_std   0.2972     0.7828   0.380    0.708
## 
## (Dispersion parameter for gaussian family taken to be 13.48234)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 283.13  on 21  degrees of freedom
## AIC: 129.01
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SDNTSS_CMPUTR_Nut801_std, family = gaussian(), 
##     data = delta_ipaq_sitting)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                0.3349     0.7456   0.449    0.658
## SDNTSS_CMPUTR_Nut801_std  -0.8679     0.7623  -1.138    0.268
## 
## (Dispersion parameter for gaussian family taken to be 12.78582)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 268.50  on 21  degrees of freedom
## AIC: 127.79
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SDNTSS_DRIVNG_Nut801_std, family = gaussian(), 
##     data = delta_ipaq_sitting)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                0.3349     0.7486   0.447    0.659
## SDNTSS_DRIVNG_Nut801_std   0.8083     0.7655   1.056    0.303
## 
## (Dispersion parameter for gaussian family taken to be 12.89046)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 270.70  on 21  degrees of freedom
## AIC: 127.98
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SDNTTT_SITTOT_Nut801_std, family = gaussian(), 
##     data = delta_ipaq_sitting)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                0.3349     0.6460   0.518  0.60956   
## SDNTTT_SITTOT_Nut801_std  -1.9486     0.6605  -2.950  0.00764 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 9.596946)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 201.54  on 21  degrees of freedom
## AIC: 121.19
## 
## Number of Fisher Scoring iterations: 2

CU A+ V-: correcting for sex, educ_years, and APOE4_load

## 
## Call:
## glm(formula = delta ~ P1_IPAQMH_PATOTL_Nut701_std, family = gaussian(), 
##     data = delta_ipaq_sitting)
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)
## (Intercept)                  0.33490    0.76823   0.436    0.667
## P1_IPAQMH_PATOTL_Nut701_std -0.02642    0.78550  -0.034    0.973
## 
## (Dispersion parameter for gaussian family taken to be 13.57416)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 285.06  on 21  degrees of freedom
## AIC: 129.17
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SDNTMF_CMPUTR_Nut801_std + sex + educ_years_std + 
##     P1_APOE4_load_std, family = gaussian(), data = delta_ipaq_sitting)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)  
## (Intercept)               0.08868    1.10292   0.080   0.9368  
## SDNTMF_CMPUTR_Nut801_std -2.31786    0.86681  -2.674   0.0155 *
## sex                       1.25166    1.47960   0.846   0.4087  
## educ_years_std            1.42300    0.88090   1.615   0.1236  
## P1_APOE4_load_std        -0.21457    0.56459  -0.380   0.7084  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 11.24359)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 202.38  on 18  degrees of freedom
## AIC: 127.29
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SDNTMF_DRIVNG_Nut801_std + sex + educ_years_std + 
##     P1_APOE4_load_std, family = gaussian(), data = delta_ipaq_sitting)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.16911    1.30022   0.130    0.898
## SDNTMF_DRIVNG_Nut801_std  0.27072    0.85373   0.317    0.755
## sex                       0.48121    1.71888   0.280    0.783
## educ_years_std            0.07039    0.87200   0.081    0.937
## P1_APOE4_load_std        -0.08833    0.66332  -0.133    0.896
## 
## (Dispersion parameter for gaussian family taken to be 15.62272)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 281.21  on 18  degrees of freedom
## AIC: 134.85
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SDNTSS_CMPUTR_Nut801_std + sex + educ_years_std + 
##     P1_APOE4_load_std, family = gaussian(), data = delta_ipaq_sitting)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)               0.46931    1.30302   0.360    0.723
## SDNTSS_CMPUTR_Nut801_std -0.90005    0.87898  -1.024    0.319
## sex                      -0.06594    1.76673  -0.037    0.971
## educ_years_std            0.21531    0.84684   0.254    0.802
## P1_APOE4_load_std        -0.11455    0.64698  -0.177    0.861
## 
## (Dispersion parameter for gaussian family taken to be 14.84525)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 267.21  on 18  degrees of freedom
## AIC: 133.68
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SDNTSS_DRIVNG_Nut801_std + sex + educ_years_std + 
##     P1_APOE4_load_std, family = gaussian(), data = delta_ipaq_sitting)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)              -0.27504    1.30556  -0.211    0.836
## SDNTSS_DRIVNG_Nut801_std  1.06683    0.89192   1.196    0.247
## sex                       1.27829    1.77127   0.722    0.480
## educ_years_std            0.31692    0.84999   0.373    0.714
## P1_APOE4_load_std         0.07985    0.65587   0.122    0.904
## 
## (Dispersion parameter for gaussian family taken to be 14.55327)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 261.96  on 18  degrees of freedom
## AIC: 133.22
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SDNTTT_SITTOT_Nut801_std + sex + educ_years_std + 
##     P1_APOE4_load_std, family = gaussian(), data = delta_ipaq_sitting)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                0.1173     1.0762   0.109  0.91439   
## SDNTTT_SITTOT_Nut801_std  -2.1345     0.7363  -2.899  0.00957 **
## sex                        0.3554     1.4204   0.250  0.80528   
## educ_years_std             0.5669     0.7312   0.775  0.44824   
## P1_APOE4_load_std          0.2259     0.5599   0.403  0.69138   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 10.71008)
## 
##     Null deviance: 285.07  on 22  degrees of freedom
## Residual deviance: 192.78  on 18  degrees of freedom
## AIC: 126.17
## 
## Number of Fisher Scoring iterations: 2

## [1] FALSE  TRUE FALSE FALSE FALSE  TRUE
## [1] 0.97348445 0.04644656 0.90577434 0.47912444 0.47912444 0.04644656

CU A- V+

## 
## Call:
## glm(formula = delta ~ P1_IPAQMH_PATOTL_Nut701_std, family = gaussian(), 
##     data = delta_ipaq_sitting)
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)
## (Intercept)                   0.3722     0.6022   0.618    0.548
## P1_IPAQMH_PATOTL_Nut701_std  -0.8406     0.6249  -1.345    0.203
## 
## (Dispersion parameter for gaussian family taken to be 5.076716)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 60.921  on 12  degrees of freedom
## AIC: 66.317
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SDNTMF_CMPUTR_Nut801_std, family = gaussian(), 
##     data = delta_ipaq_sitting)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                0.3722     0.6370   0.584    0.570
## SDNTMF_CMPUTR_Nut801_std  -0.3854     0.6611  -0.583    0.571
## 
## (Dispersion parameter for gaussian family taken to be 5.681346)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 68.176  on 12  degrees of freedom
## AIC: 67.893
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SDNTMF_DRIVNG_Nut801_std, family = gaussian(), 
##     data = delta_ipaq_sitting)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                0.3722     0.6182   0.602    0.558
## SDNTMF_DRIVNG_Nut801_std   0.6743     0.6415   1.051    0.314
## 
## (Dispersion parameter for gaussian family taken to be 5.349711)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 64.197  on 12  degrees of freedom
## AIC: 67.051
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SDNTSS_CMPUTR_Nut801_std, family = gaussian(), 
##     data = delta_ipaq_sitting)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                0.3722     0.5774   0.645    0.531
## SDNTSS_CMPUTR_Nut801_std   1.0411     0.5992   1.737    0.108
## 
## (Dispersion parameter for gaussian family taken to be 4.667939)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 56.015  on 12  degrees of freedom
## AIC: 65.142
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SDNTSS_DRIVNG_Nut801_std, family = gaussian(), 
##     data = delta_ipaq_sitting)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                0.3722     0.5901   0.631    0.540
## SDNTSS_DRIVNG_Nut801_std   0.9445     0.6124   1.542    0.149
## 
## (Dispersion parameter for gaussian family taken to be 4.875813)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 58.510  on 12  degrees of freedom
## AIC: 65.752
## 
## Number of Fisher Scoring iterations: 2
## 
## Call:
## glm(formula = delta ~ SDNTTT_SITTOT_Nut801_std, family = gaussian(), 
##     data = delta_ipaq_sitting)
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)
## (Intercept)                0.3722     0.6406   0.581    0.572
## SDNTTT_SITTOT_Nut801_std   0.2999     0.6648   0.451    0.660
## 
## (Dispersion parameter for gaussian family taken to be 5.744769)
## 
##     Null deviance: 70.107  on 13  degrees of freedom
## Residual deviance: 68.937  on 12  degrees of freedom
## AIC: 68.048
## 
## Number of Fisher Scoring iterations: 2

Takeaway

Significant negative association between sedentarism score and CAD in CU A+ V- group. Interpretation is not intuitive a priori ā€œthe more hours you sit, the less CAD you have (less cognitive aging)ā€. However, we see consistent associations with education years and CAD in the sense of ā€œmore education years means less cognitive agingā€, and perhaps the sitting hours are employed in cognitively stimulating tasks for those who sit more.

Negative statistically significant effect of sitting hours in front of computer on delta in CU A+ V- group, even after correcting for sex, education years, and APOE4 load. ā€œThe more you sit in front of the computer from Monday to Friday, the less cognitive aging you have.ā€

Do those who sit more in front of the computer from Monday to Friday also have more education years?

Relationship between education years and sitting hours in front of the computer from Monday to Friday

CU A- V-

CU A+ V-

CU A+ V-

All groups

CSF signal association with CAD

CSF signals in the whole group.

CSF signals in the CU A- V- group

CSF signals in the CU A+ V- group

CSF signals in the CU A- V+ group

### Comparison of participants with CSF and no CSF

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  data_csf$Age and data_no_csf$Age
## W = 21426, p-value = 0.4627
## alternative hypothesis: true location shift is not equal to 0
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  .
## X-squared = 2.0312, df = 1, p-value = 0.1541
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  data_csf$Educ_years and data_no_csf$Educ_years
## W = 19698, p-value = 0.4692
## alternative hypothesis: true location shift is not equal to 0
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  .
## X-squared = 0.0053023, df = 1, p-value = 0.942

Ideas

After correcting for years of education, all possible associations are thrown off. The factor that is most associated with years of education, vocabulary, is also most associated with CADs, suggesting that our cognitive age delta captures this information, more intensely than any other cognitive domains.

The association of sedentarism and CADs in CU A+ V- could (perhaps?) be explained as noted above, stating that the sitting time is cognitively stimulating/meaningful. However this does not hold for the CU A- V- groups, what makes it more difficult to justify.

Also, it must be noted that our sample size is very reduced in all clinical groups, which makes drawing conclusions very challenging and a thing to do with utmost caution. Studies using this methodology typically use bigger sample sizes accross all their clinical groups, and particularly in the reference group (CU A- V- in our case).